Author Topic: Deconvolution Challenge  (Read 17979 times)

Offline astroedo

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Re: Deconvolution Challenge
« Reply #15 on: 2014 April 27 06:08:25 »
Juan

Thats a very useful decon walkthrough. One question - I realise the local support image is not a mask but should it be inverted (white background) prior to be used in the deconvolution tool?

Chris

I'm not Juan, but I think that the right answer in NO, you do not have to invert the local deringing support: such a "mask" tells to the deconvolution process how much deringing has to be applied locally.
You need much more deringing around the stars, so stars should be white on a dark background where local deringing is not needed and only global deringing applies.

Is it correct Juan?

bye

Edoardo

Offline Juan Conejero

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Re: Deconvolution Challenge
« Reply #16 on: 2014 April 28 09:20:42 »
Yes, absolutely correct.
Juan Conejero
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Offline Ignacio

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Re: Deconvolution Challenge
« Reply #17 on: 2014 April 28 10:47:15 »
Very interesting deconv example, Juan, thank you.

Two things called my attention. First, the "unusual" way of creating a star mask for deringing support, which begs the question: what should we expect from the new/revised  StarMask module?
Second: I typically build a support mask to cover the bigger/brightest stars, but I see in your example that pretty much all stars are supported. On the positive side, I see that this prevents rings around them, but on the negative side they are (apparently) not reduced in size. In my experience with deconv, medium and small size stars are nicely reduced (fwhm cut in half) if not supported, and without darks rings around them. In some cases, when such stars are embedded in a region with even and relatively bright background, then it is harder to come up with the proper deringing parameters if unsupported, and there is always a tradeoff.

Thoughts/comments?

Ignacio

Offline Juan Conejero

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Re: Deconvolution Challenge
« Reply #18 on: 2014 April 28 11:24:13 »
Thanks Ignacio. The StarMask tool has three well-known problems:

- It is very slow.
- It requires a lot of trial/error work.
- It is not previewable.

These limitations cause StarMask to be difficult to use in many practical cases, even with modern hardware. This obviously has to be addressed with a revision of the tool. If I remember well, I wrote StarMask at least seven years ago, so a complete redesign/reimplementation is in order.

Processing wise, stars and other high-contrast, small-scale structures can be considered as singularities where most image processing algorithms fail or are not applicable. Consequently, having efficient tools to isolate stars is of the highest importance. We already have multiscale analysis tools that, if creatively used, allow for very efficient generation of star masks, and I just wanted to show a practical example in this tutorial. I probably was too exhaustive in the description of these techniques, and hence the generated deringing support is excessive. As you point out, the key word here is to find a compromise between ringing suppression and deconvolution efficiency.
Juan Conejero
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Offline Ignacio

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Re: Deconvolution Challenge
« Reply #19 on: 2014 April 28 11:33:29 »
Understood, thanks. Looking forward to the new tool.

Ignacio

Offline jeffweiss9

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Re: Deconvolution Challenge
« Reply #20 on: 2014 April 28 21:33:19 »
Juan-   Update
  Ok, I worked through it all in detail and found it was a very good tutorial.  I believe I have a good feeling now for what needs to be done to make it work well, although the test will come in applying it on very different data that requires parameters that may be far from these initial values. But I think it gave me a good appreciation of the intermediate goals for each step, independent of the parameters, so I'm hopeful I now will be able to achieve consistent results.
Thanks for the great tutorial.
Clear skies,
-Jeff
« Last Edit: 2014 April 29 07:08:46 by jeffweiss9 »
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Offline Carlos Milovic

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Re: Deconvolution Challenge
« Reply #21 on: 2014 July 07 15:19:40 »
Just a quick note on the news from the development team:
- Our first implementation of TGVRestoration was modeled with a Gaussian noise distribution as basis. So, it worked very well with  high SNR images, and "normal" images. Astronomical deep sky images, on the other hand, suffered from some serious ringing. We included some deringing functions to mitigate this problem, and achieve more robust results, but were not as good as I expected (and I had very high expectations for this... I think that it is better than regularized RL, but more work is needed).
- The tests on deep sky images derived in a reformulation of the TGV regularization algorithm, both for the Denoise and Restoration problems. A new method was designed for a Poisson noise model. Right now we are close to publish a new TGVDenoise, with several changes. Now the tool is much more flexible, and may adapt its behavior for many noise distributions. It has in-built 3 statistical noise models, Gaussian, Poisson and a L1 norm, with a new flexible edge protection. We are polishing the interface elements and working on some examples to accompany the new release.
- The development of the TGVRestoration tool has being delayed a couple of weeks, until we have the new TGVDenoise ready for release. The new TGVR will also incorporate several statistical models. Right now we are testing a mixed L1/L2 Norm (for Gaussian noise), and three solvers for the Poisson model. One of them is a regularized Richardson-Lucy iteration, with a modified gradient, following the classic TV algorithm. Another is a two step Expectation-Maximization (similar to regularized RL), with a TGV step. The last is a new derivation of the Chambolle/Pock primal dual algorithm for Poisson noise. So, a lot is going on here.

Please, have a little more patience. We are working to create a very powerful and flexible tool. We still need to evaluate the efficiency of our new deringing methods, so more time is needed. I may publish an unofficial release in my development server, asking for beta testing. Stay tuned.

Regards,

Carlos Milovic F.
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Offline Philippe B.

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Re: Deconvolution Challenge
« Reply #22 on: 2014 October 27 09:47:08 »
Hello PI team  O0


Should incoming 1.8.3 see the new TGVRestoration process ?


Best
Philippe

Offline Carlos Milovic

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Re: Deconvolution Challenge
« Reply #23 on: 2014 October 28 01:06:52 »
Hi Phillipe.

Right now we are working on the last modifications to TGVDenoise. There are a lot of changes. We are evaluating some critical changes to the interfase and inner working of some parameters.
Also we have the inpainting tool based on tgv that needs a small review.
Sideways, I wrote the first documentation of TGVDenoise, for the current release. It may be online in the next days or couple of weeks.

Development of TGVRestoration should continue this december. I made huge changes to the code also, that needs a lot of testing. Unfortunatelly, I'm quite busy with other projects right now, so it will have to wait a little while.


Thanks for your patience! I will consider to release an alpha version of TGVR if I see that it works without problems in the first tests.
Regards,

Carlos Milovic F.
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Offline Philippe B.

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Re: Deconvolution Challenge
« Reply #24 on: 2014 October 29 04:28:55 »
Hi Carlos
Thank you for your work ! I'm sure new TGVDenoise will be very nice and I will wait for TGVR (but if you want I test some alpha version, please do not hesitate)


Cheers